Does the 2080ti support bfloat16?

Sources

[D] Does the Geforce RTX 3000 series GPU support bfloat16 ...Sep 2, 2020 ... Anyone willing to spent $1200 on a 2080 Ti Nvidia clearly wants to push to the $1500 RTX 3090 instead of the $699 RTX 3080 as well, so that also ...1
Bfloat16 native support - PyTorch ForumsApr 5, 2021 ... I have a few questions about bfloat16 how can I tell via pytorch if the gpu it's running on supports bf16 natively?2
How to use Optimizer State Sharding with Sharpness-Aware ...May 20, 2022 ... I tried bfloat16 on both 2080 Ti and V100 using autocast(dtype=torch. ... RuntimeError: Current CUDA Device does not support bfloat16. Please ...3
FP64, FP32, FP16, BFLOAT16, TF32, and other members of the ...May 16, 2020 ... GPU: Supported in NVIDIA A100 (first one to support), will be supported in future AMD GPUs. ASIC: Supported in Google TPU v2/v3 (not v1!), ...4
How can i run the model on RTX 2080 Ti gpu · Issue #168 · THUDM ...Dec 9, 2023 ... can you help about it? i stuck... i am getting this error ... dtype=torch.bfloat16 (supported: {torch.float32}) max(query.shape[-1] ...5
NVIDIA ADA GPU ARCHITECTURE - Designed to deliver ...Ada's opacity mask support can significantly increase the amount and fidelity of ... BFLOAT16 Tensor Core. Performance. 149.7 | 299.4 TFLOPS. 181 | 362 TFLOPS1.6
ValueError: Bfloat16 is only supported on GPUs with compute ...Sep 22, 2023 ... ValueError: Bfloat16 is only supported on GPUs with compute capability of at least 8.0. Your Tesla T4 GPU has compute capability 7.5. #1157.7
Tensor Cores: Versatility for HPC & AI | NVIDIAAnd with support for bfloat16, INT8, and INT4, these third-generation Tensor ... Do Not Sell or Share My Data · Legal · Accessibility · Corporate Policies ...8
Feature request: bfloat16 support for CUDA matmuls · Issue #67682 ...Nov 2, 2021 ... does torch.matmul not support BFloat16Tensor? cc ... This is unexpected as we do support bfloat16 matrix multiplications on CUDA 11 or later.9
torch.set_float32_matmul_precision — PyTorch 2.1 documentationSupports three settings: “highest”, float32 matrix multiplications use ... bfloat16 precision than it is to do a single multiplication with float32 precision.10
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Answer

The NVIDIA GeForce RTX 2080 Ti GPU does not support bfloat16 (BF16) natively. This is evident from multiple sources and discussions, including comments on PyTorch forums and GitHub issues related to CUDA errors when using bfloat16. It's important to note that bfloat16 is mainly supported in newer GPUs, such as the NVIDIA A100, and future AMD GPUs, as well as in certain ASICs like Google TPU v2/v3.

While the 2080 Ti is a powerful GPU with impressive capabilities for deep learning and AI, native support for bfloat16 is not part of its feature set. When using frameworks like PyTorch and TensorFlow, it's crucial to consider the hardware compatibility for operations involving bfloat16 to ensure optimal performance and accuracy.

In summary, if you are specifically looking to leverage bfloat16 precision in your deep learning workflows, it would be advisable to consider GPUs with native support for bfloat16, such as the NVIDIA A100, and future AMD GPUs, to fully maximize the benefits of this reduced-precision format.

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